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My Python Development Environment, 2020 Edition - Jacob Kaplan-Moss
I wrote this post in 2019, more than 6 years ago. It may be very out of date, partially or totally incorrect. I may even no longer agree with this, or might approach things differently if I wrote this post today. I rarely edit posts after writing them, but if I have there'll be a note at the bottom about what I changed and why. If something in this post is actively harmful or dangerous please get
GitHub - polynote/polynote: A better notebook for Scala (and more)
Polynote is an experimental polyglot notebook environment. Currently, it supports Scala and Python (with or without Spark), SQL, and Vega. For more information, see Polynote's website Current notebook solutions, like Jupyter and Zeppelin, are lacking in some fundamental features: Code editing – the code editing capabilities in most notebook tools leave plenty to be desired. Why can't a notebook to
GitHub - idealo/imagededup: 😎 Finding duplicate images made easy!
imagededup is a python package that simplifies the task of finding exact and near duplicates in an image collection. This package provides functionality to make use of hashing algorithms that are particularly good at finding exact duplicates as well as convolutional neural networks which are also adept at finding near duplicates. An evaluation framework is also provided to judge the quality of ded
Our journey to type checking 4 million lines of Python
Dropbox is a big user of Python. It’s our most widely used language both for backend services and the desktop client app (we are also heavy users of Go, TypeScript, and Rust). At our scale—millions of lines of Python—the dynamic typing in Python made code needlessly hard to understand and started to seriously impact productivity. To mitigate this, we have been gradually migrating our code to stati
Choosing a Fast Python API Framework
Posted on May 17, 2018 | 6 minutes | Fotis Gimian This post attempts to highlight my thought process in selecting a suitable stack for developing an API in Python for our current project at work. Although I have personally benchmarked various combinations, I haven’t documented the results for this article, instead merely mentioned which frameworks and WSGI servers were found to be fast or slow.
GitHub - lyst/lightfm: A Python implementation of LightFM, a hybrid recommendation algorithm.
LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization a
GitHub - timothycrosley/portray: Your Project with Great Documentation.
Read Latest Documentation - Browse GitHub Code Repository The only thing worse than documentation never written, is documentation written but never discovered. portray is a Python3 command line tool and library that helps you create great documentation websites for your Python projects with as little effort as possible. Key Features: Zero-Config: No configuration is necessary to use portray. Just
Python is eating the world: How one developer's side project became the hottest programming language on the planet | TechRepublic
We may earn from vendors via affiliate links or sponsorships. This might affect product placement on our site, but not the content of our reviews. See our Terms of Use for details. Frustrated by programming language shortcomings, Guido van Rossum created Python. With the language now used by millions, Nick Heath talks to van Rossum about Python's past and explores what's next. In late 1994, a sele
Practical Data Science with R and Python: 実践的データサイエンス
実践的データサイエンス はじめに データ分析のためにコンピュータを利用する際、RおよびPython言語のいずれかを使うことが多いと思います(Julia言語は高レベル・高パフォーマンスな技術計算のための言語で今後期待が膨らみます）。これらの2つの言語では、データ操作や可視化、データ分析、モデリングに使われるライブラリが豊富にあり、 どれを使うのが良いのか迷うような状況が続いていました。しかしその状態は落ち着きを見せ、成熟期を迎えつつあります。 R言語ではパイプ演算子の登場によりデータフレームに対する操作に大きな変化が生じ、tidyverseによるデータ読み込みからデータ整形、可視化までが可能になりました。またtidyverseのような、機械や人間の双方が扱いやすいパッケージが増えてきました。特にR言語の強力な一面でもあったデータ分析の操作はtidymodelsに代表されるパッケージがユーザの
退屈なことはPythonにやらせる...前に考えるべきこと - Lean Baseball
機械学習, データサイエンスを志向してPythonを勉強したり,この辺のエントリーで本を読み漁ったりすると*1, もしかして, 私の仕事ってプログラミングで楽になるのでは！？ と気がつく(もしくはそういう記事・本を読んで触発される)瞬間があるかと思います, この本とかあの本とか. このエントリーではそんな素晴らしい学び*2に対して,一つの回答を示してみたいと思います. TL;DR（ここは読んでほしい） プログラミングで解決できる「退屈なこと」とは「回数が多く, 属人性が少ない」作業のことである. 属人性がある仕事はプログラミングをする前に因数分解しよう or 「仕事ごっこ」だったらやめる努力をしよう. Pythonで「退屈なこと」をやるなら,「退屈なことはPythonにやらせよう」もいいですが,「できる仕事がはかどるPython自動処理 全部入り。」が個人的にはおすすめです. 言いたいこと
GitHub - prompt-toolkit/python-prompt-toolkit: Library for building powerful interactive command line applications in Python
prompt_toolkit could be a replacement for GNU readline, but it can be much more than that. Some features: Pure Python. Syntax highlighting of the input while typing. (For instance, with a Pygments lexer.) Multi-line input editing. Advanced code completion. Both Emacs and Vi key bindings. (Similar to readline.) Even some advanced Vi functionality, like named registers and digraphs. Reverse and forw
GitHub - indygreg/PyOxidizer: A modern Python application packaging and distribution tool
PyOxidizer is a utility for producing binaries that embed Python. The over-arching goal of PyOxidizer is to make complex packaging and distribution problems simple so application maintainers can focus on building applications instead of toiling with build systems and packaging tools. PyOxidizer is capable of producing a single file executable - with a copy of Python and all its dependencies static
「データ分析をやるならRとPythonのどちらを使うべき？」への個人的な回答 - 渋谷駅前で働くデータサイエンティストのブログ
(Background image by Pixabay) 最近また「データ分析をやるならRとPythonのどちらでやるべきか」という話題が出ていたようです。 言語仕様やその他の使い勝手という点では、大体この記事に書いてあることを参考にすれば良いと思います。その上で、人には当然ながら趣味嗜好がありますので、個々人が好みだと思う方を使えば良い話ではあります。 とは言え、僕自身もクソコードの羅列ながらこのブログにR & Pythonのコードを載せているということもあるので、便乗して今回の記事では僕個人の意見と感想も書いてみようと思います。いつもながらど素人の意見（特にPythonは本業ではない）なのと、自分がメインに使っているRでもtidyverseをほとんど使わないなど割とout-of-dateな使い方をしているということもあり、読んでいておかしなところなどあればどしどしご指摘くださると有難
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GitHub - julvo/reloading: Change Python code while it's running without losing state
Pythonic News
https://python.cs.southern.edu/pythonbook/pythonbook.pdf
Client Challenge
GitHub - seemoo-lab/opendrop: An open Apple AirDrop implementation written in Python
GitHub - ssut/py-googletrans: (unofficial) Googletrans: Free and Unlimited Google translate API for Python. Translates totally free of charge.
